{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# softmax 多分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/300\n",
      "7/7 [==============================] - 1s 55ms/step - loss: 1.1557 - acc: 0.2685 - val_loss: 1.1497 - val_acc: 0.0000e+00\n",
      "Epoch 2/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 1.1010 - acc: 0.3704 - val_loss: 1.1058 - val_acc: 0.5000\n",
      "Epoch 3/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 1.0527 - acc: 0.4907 - val_loss: 1.0657 - val_acc: 0.5000\n",
      "Epoch 4/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 1.0058 - acc: 0.5741 - val_loss: 1.0278 - val_acc: 0.5000\n",
      "Epoch 5/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.9621 - acc: 0.6389 - val_loss: 0.9934 - val_acc: 0.5000\n",
      "Epoch 6/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.9235 - acc: 0.6759 - val_loss: 0.9607 - val_acc: 0.5000\n",
      "Epoch 7/300\n",
      "7/7 [==============================] - 0s 16ms/step - loss: 0.8857 - acc: 0.7222 - val_loss: 0.9314 - val_acc: 0.5000\n",
      "Epoch 8/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.8524 - acc: 0.7222 - val_loss: 0.9046 - val_acc: 0.5000\n",
      "Epoch 9/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.8196 - acc: 0.7315 - val_loss: 0.8805 - val_acc: 0.5000\n",
      "Epoch 10/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.7904 - acc: 0.7500 - val_loss: 0.8599 - val_acc: 0.5000\n",
      "Epoch 11/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.7622 - acc: 0.7593 - val_loss: 0.8415 - val_acc: 0.5000\n",
      "Epoch 12/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.7367 - acc: 0.7685 - val_loss: 0.8241 - val_acc: 0.5000\n",
      "Epoch 13/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.7119 - acc: 0.7685 - val_loss: 0.8062 - val_acc: 0.5000\n",
      "Epoch 14/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.6892 - acc: 0.7778 - val_loss: 0.7882 - val_acc: 0.5000\n",
      "Epoch 15/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.6677 - acc: 0.7870 - val_loss: 0.7725 - val_acc: 0.5000\n",
      "Epoch 16/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.6466 - acc: 0.7870 - val_loss: 0.7552 - val_acc: 0.5000\n",
      "Epoch 17/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.6264 - acc: 0.7870 - val_loss: 0.7387 - val_acc: 1.0000\n",
      "Epoch 18/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.6077 - acc: 0.7963 - val_loss: 0.7213 - val_acc: 1.0000\n",
      "Epoch 19/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5892 - acc: 0.7963 - val_loss: 0.7055 - val_acc: 1.0000\n",
      "Epoch 20/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5726 - acc: 0.8056 - val_loss: 0.6892 - val_acc: 1.0000\n",
      "Epoch 21/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.5566 - acc: 0.8241 - val_loss: 0.6737 - val_acc: 1.0000\n",
      "Epoch 22/300\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.5409 - acc: 0.8241 - val_loss: 0.6583 - val_acc: 1.0000\n",
      "Epoch 23/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.5265 - acc: 0.8333 - val_loss: 0.6426 - val_acc: 1.0000\n",
      "Epoch 24/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.5127 - acc: 0.8426 - val_loss: 0.6276 - val_acc: 1.0000\n",
      "Epoch 25/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.4994 - acc: 0.8519 - val_loss: 0.6132 - val_acc: 1.0000\n",
      "Epoch 26/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.4872 - acc: 0.8519 - val_loss: 0.5983 - val_acc: 1.0000\n",
      "Epoch 27/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.4753 - acc: 0.8611 - val_loss: 0.5842 - val_acc: 1.0000\n",
      "Epoch 28/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.4640 - acc: 0.8611 - val_loss: 0.5707 - val_acc: 1.0000\n",
      "Epoch 29/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.4537 - acc: 0.8611 - val_loss: 0.5578 - val_acc: 1.0000\n",
      "Epoch 30/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.4434 - acc: 0.8611 - val_loss: 0.5437 - val_acc: 1.0000\n",
      "Epoch 31/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.4337 - acc: 0.8611 - val_loss: 0.5305 - val_acc: 1.0000\n",
      "Epoch 32/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.4245 - acc: 0.8704 - val_loss: 0.5187 - val_acc: 1.0000\n",
      "Epoch 33/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.4156 - acc: 0.8704 - val_loss: 0.5076 - val_acc: 1.0000\n",
      "Epoch 34/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.4074 - acc: 0.8704 - val_loss: 0.4961 - val_acc: 1.0000\n",
      "Epoch 35/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.3997 - acc: 0.8704 - val_loss: 0.4839 - val_acc: 1.0000\n",
      "Epoch 36/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.3924 - acc: 0.8796 - val_loss: 0.4728 - val_acc: 1.0000\n",
      "Epoch 37/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.3851 - acc: 0.8796 - val_loss: 0.4626 - val_acc: 1.0000\n",
      "Epoch 38/300\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.3785 - acc: 0.8611 - val_loss: 0.4521 - val_acc: 1.0000\n",
      "Epoch 39/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.3720 - acc: 0.8611 - val_loss: 0.4429 - val_acc: 1.0000\n",
      "Epoch 40/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.3661 - acc: 0.8611 - val_loss: 0.4341 - val_acc: 1.0000\n",
      "Epoch 41/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.3602 - acc: 0.8611 - val_loss: 0.4264 - val_acc: 1.0000\n",
      "Epoch 42/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.3547 - acc: 0.8704 - val_loss: 0.4182 - val_acc: 1.0000\n",
      "Epoch 43/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.3495 - acc: 0.8704 - val_loss: 0.4105 - val_acc: 1.0000\n",
      "Epoch 44/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.3443 - acc: 0.8704 - val_loss: 0.4025 - val_acc: 1.0000\n",
      "Epoch 45/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.3393 - acc: 0.8704 - val_loss: 0.3940 - val_acc: 1.0000\n",
      "Epoch 46/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.3346 - acc: 0.8704 - val_loss: 0.3869 - val_acc: 1.0000\n",
      "Epoch 47/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.3300 - acc: 0.8796 - val_loss: 0.3800 - val_acc: 1.0000\n",
      "Epoch 48/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.3256 - acc: 0.8889 - val_loss: 0.3725 - val_acc: 1.0000\n",
      "Epoch 49/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.3213 - acc: 0.8889 - val_loss: 0.3660 - val_acc: 1.0000\n",
      "Epoch 50/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.3171 - acc: 0.8889 - val_loss: 0.3595 - val_acc: 1.0000\n",
      "Epoch 51/300\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.3131 - acc: 0.8889 - val_loss: 0.3533 - val_acc: 1.0000\n",
      "Epoch 52/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.3094 - acc: 0.8889 - val_loss: 0.3470 - val_acc: 1.0000\n",
      "Epoch 53/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.3056 - acc: 0.8889 - val_loss: 0.3404 - val_acc: 1.0000\n",
      "Epoch 54/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.3027 - acc: 0.8889 - val_loss: 0.3328 - val_acc: 1.0000\n",
      "Epoch 55/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.2987 - acc: 0.8889 - val_loss: 0.3278 - val_acc: 1.0000\n",
      "Epoch 56/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.2956 - acc: 0.8889 - val_loss: 0.3217 - val_acc: 1.0000\n",
      "Epoch 57/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.2921 - acc: 0.8889 - val_loss: 0.3167 - val_acc: 1.0000\n",
      "Epoch 58/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.2888 - acc: 0.8889 - val_loss: 0.3120 - val_acc: 1.0000\n",
      "Epoch 59/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.2859 - acc: 0.8889 - val_loss: 0.3076 - val_acc: 1.0000\n",
      "Epoch 60/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.2829 - acc: 0.8889 - val_loss: 0.3035 - val_acc: 1.0000\n",
      "Epoch 61/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.2799 - acc: 0.8981 - val_loss: 0.2996 - val_acc: 1.0000\n",
      "Epoch 62/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.2770 - acc: 0.8981 - val_loss: 0.2956 - val_acc: 1.0000\n",
      "Epoch 63/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.2743 - acc: 0.8981 - val_loss: 0.2909 - val_acc: 1.0000\n",
      "Epoch 64/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.2715 - acc: 0.8981 - val_loss: 0.2868 - val_acc: 1.0000\n",
      "Epoch 65/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.2689 - acc: 0.8981 - val_loss: 0.2833 - val_acc: 1.0000\n",
      "Epoch 66/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.2665 - acc: 0.8981 - val_loss: 0.2786 - val_acc: 1.0000\n",
      "Epoch 67/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.2636 - acc: 0.8981 - val_loss: 0.2754 - val_acc: 1.0000\n",
      "Epoch 68/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.2612 - acc: 0.8981 - val_loss: 0.2727 - val_acc: 1.0000\n",
      "Epoch 69/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.2587 - acc: 0.8981 - val_loss: 0.2687 - val_acc: 1.0000\n",
      "Epoch 70/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.2563 - acc: 0.8981 - val_loss: 0.2647 - val_acc: 1.0000\n",
      "Epoch 71/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.2537 - acc: 0.8981 - val_loss: 0.2621 - val_acc: 1.0000\n",
      "Epoch 72/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.2516 - acc: 0.8981 - val_loss: 0.2597 - val_acc: 1.0000\n",
      "Epoch 73/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.2491 - acc: 0.8981 - val_loss: 0.2562 - val_acc: 1.0000\n",
      "Epoch 74/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.2468 - acc: 0.8981 - val_loss: 0.2521 - val_acc: 1.0000\n",
      "Epoch 75/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.2449 - acc: 0.8981 - val_loss: 0.2489 - val_acc: 1.0000\n",
      "Epoch 76/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.2426 - acc: 0.8981 - val_loss: 0.2464 - val_acc: 1.0000\n",
      "Epoch 77/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.2405 - acc: 0.8981 - val_loss: 0.2431 - val_acc: 1.0000\n",
      "Epoch 78/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.2382 - acc: 0.8981 - val_loss: 0.2398 - val_acc: 1.0000\n",
      "Epoch 79/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.2361 - acc: 0.9074 - val_loss: 0.2367 - val_acc: 1.0000\n",
      "Epoch 80/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.2340 - acc: 0.9074 - val_loss: 0.2341 - val_acc: 1.0000\n",
      "Epoch 81/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.2321 - acc: 0.9074 - val_loss: 0.2319 - val_acc: 1.0000\n",
      "Epoch 82/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.2305 - acc: 0.9074 - val_loss: 0.2303 - val_acc: 1.0000\n",
      "Epoch 83/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.2284 - acc: 0.9074 - val_loss: 0.2262 - val_acc: 1.0000\n",
      "Epoch 84/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.2262 - acc: 0.9074 - val_loss: 0.2246 - val_acc: 1.0000\n",
      "Epoch 85/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.2242 - acc: 0.9074 - val_loss: 0.2214 - val_acc: 1.0000\n",
      "Epoch 86/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.2222 - acc: 0.9074 - val_loss: 0.2184 - val_acc: 1.0000\n",
      "Epoch 87/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.2204 - acc: 0.9074 - val_loss: 0.2150 - val_acc: 1.0000\n",
      "Epoch 88/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.2184 - acc: 0.9074 - val_loss: 0.2123 - val_acc: 1.0000\n",
      "Epoch 89/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.2167 - acc: 0.9074 - val_loss: 0.2096 - val_acc: 1.0000\n",
      "Epoch 90/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.2150 - acc: 0.9074 - val_loss: 0.2077 - val_acc: 1.0000\n",
      "Epoch 91/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.2132 - acc: 0.9167 - val_loss: 0.2042 - val_acc: 1.0000\n",
      "Epoch 92/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.2114 - acc: 0.9167 - val_loss: 0.2018 - val_acc: 1.0000\n",
      "Epoch 93/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.2095 - acc: 0.9167 - val_loss: 0.1987 - val_acc: 1.0000\n",
      "Epoch 94/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.2082 - acc: 0.9167 - val_loss: 0.1962 - val_acc: 1.0000\n",
      "Epoch 95/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.2063 - acc: 0.9259 - val_loss: 0.1939 - val_acc: 1.0000\n",
      "Epoch 96/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.2043 - acc: 0.9259 - val_loss: 0.1914 - val_acc: 1.0000\n",
      "Epoch 97/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.2027 - acc: 0.9259 - val_loss: 0.1880 - val_acc: 1.0000\n",
      "Epoch 98/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.2009 - acc: 0.9352 - val_loss: 0.1860 - val_acc: 1.0000\n",
      "Epoch 99/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.1992 - acc: 0.9352 - val_loss: 0.1829 - val_acc: 1.0000\n",
      "Epoch 100/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1975 - acc: 0.9352 - val_loss: 0.1813 - val_acc: 1.0000\n",
      "Epoch 101/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1959 - acc: 0.9352 - val_loss: 0.1785 - val_acc: 1.0000\n",
      "Epoch 102/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.1942 - acc: 0.9352 - val_loss: 0.1760 - val_acc: 1.0000\n",
      "Epoch 103/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1926 - acc: 0.9352 - val_loss: 0.1744 - val_acc: 1.0000\n",
      "Epoch 104/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1909 - acc: 0.9352 - val_loss: 0.1719 - val_acc: 1.0000\n",
      "Epoch 105/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1892 - acc: 0.9352 - val_loss: 0.1692 - val_acc: 1.0000\n",
      "Epoch 106/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1878 - acc: 0.9352 - val_loss: 0.1665 - val_acc: 1.0000\n",
      "Epoch 107/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.1866 - acc: 0.9352 - val_loss: 0.1646 - val_acc: 1.0000\n",
      "Epoch 108/300\n",
      "7/7 [==============================] - 0s 7ms/step - loss: 0.1848 - acc: 0.9352 - val_loss: 0.1606 - val_acc: 1.0000\n",
      "Epoch 109/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1831 - acc: 0.9352 - val_loss: 0.1591 - val_acc: 1.0000\n",
      "Epoch 110/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.1815 - acc: 0.9444 - val_loss: 0.1567 - val_acc: 1.0000\n",
      "Epoch 111/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.1801 - acc: 0.9444 - val_loss: 0.1539 - val_acc: 1.0000\n",
      "Epoch 112/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.1786 - acc: 0.9444 - val_loss: 0.1520 - val_acc: 1.0000\n",
      "Epoch 113/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1771 - acc: 0.9444 - val_loss: 0.1496 - val_acc: 1.0000\n",
      "Epoch 114/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.1756 - acc: 0.9444 - val_loss: 0.1473 - val_acc: 1.0000\n",
      "Epoch 115/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.1742 - acc: 0.9444 - val_loss: 0.1448 - val_acc: 1.0000\n",
      "Epoch 116/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.1729 - acc: 0.9444 - val_loss: 0.1428 - val_acc: 1.0000\n",
      "Epoch 117/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1723 - acc: 0.9444 - val_loss: 0.1421 - val_acc: 1.0000\n",
      "Epoch 118/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.1701 - acc: 0.9444 - val_loss: 0.1394 - val_acc: 1.0000\n",
      "Epoch 119/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1687 - acc: 0.9444 - val_loss: 0.1370 - val_acc: 1.0000\n",
      "Epoch 120/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.1674 - acc: 0.9444 - val_loss: 0.1351 - val_acc: 1.0000\n",
      "Epoch 121/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.1662 - acc: 0.9444 - val_loss: 0.1330 - val_acc: 1.0000\n",
      "Epoch 122/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1648 - acc: 0.9444 - val_loss: 0.1299 - val_acc: 1.0000\n",
      "Epoch 123/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1636 - acc: 0.9444 - val_loss: 0.1273 - val_acc: 1.0000\n",
      "Epoch 124/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1624 - acc: 0.9444 - val_loss: 0.1262 - val_acc: 1.0000\n",
      "Epoch 125/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1609 - acc: 0.9444 - val_loss: 0.1247 - val_acc: 1.0000\n",
      "Epoch 126/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1598 - acc: 0.9444 - val_loss: 0.1235 - val_acc: 1.0000\n",
      "Epoch 127/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.1586 - acc: 0.9444 - val_loss: 0.1215 - val_acc: 1.0000\n",
      "Epoch 128/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1573 - acc: 0.9444 - val_loss: 0.1199 - val_acc: 1.0000\n",
      "Epoch 129/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1563 - acc: 0.9444 - val_loss: 0.1176 - val_acc: 1.0000\n",
      "Epoch 130/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1552 - acc: 0.9444 - val_loss: 0.1163 - val_acc: 1.0000\n",
      "Epoch 131/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.1539 - acc: 0.9444 - val_loss: 0.1145 - val_acc: 1.0000\n",
      "Epoch 132/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1530 - acc: 0.9444 - val_loss: 0.1131 - val_acc: 1.0000\n",
      "Epoch 133/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1516 - acc: 0.9444 - val_loss: 0.1111 - val_acc: 1.0000\n",
      "Epoch 134/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1505 - acc: 0.9444 - val_loss: 0.1095 - val_acc: 1.0000\n",
      "Epoch 135/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.1491 - acc: 0.9444 - val_loss: 0.1074 - val_acc: 1.0000\n",
      "Epoch 136/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.1480 - acc: 0.9444 - val_loss: 0.1056 - val_acc: 1.0000\n",
      "Epoch 137/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1468 - acc: 0.9444 - val_loss: 0.1038 - val_acc: 1.0000\n",
      "Epoch 138/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.1458 - acc: 0.9444 - val_loss: 0.1025 - val_acc: 1.0000\n",
      "Epoch 139/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.1447 - acc: 0.9444 - val_loss: 0.1011 - val_acc: 1.0000\n",
      "Epoch 140/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1436 - acc: 0.9444 - val_loss: 0.0999 - val_acc: 1.0000\n",
      "Epoch 141/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1427 - acc: 0.9444 - val_loss: 0.0980 - val_acc: 1.0000\n",
      "Epoch 142/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.1414 - acc: 0.9444 - val_loss: 0.0965 - val_acc: 1.0000\n",
      "Epoch 143/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1404 - acc: 0.9537 - val_loss: 0.0949 - val_acc: 1.0000\n",
      "Epoch 144/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.1393 - acc: 0.9537 - val_loss: 0.0931 - val_acc: 1.0000\n",
      "Epoch 145/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.1384 - acc: 0.9537 - val_loss: 0.0921 - val_acc: 1.0000\n",
      "Epoch 146/300\n",
      "7/7 [==============================] - 0s 13ms/step - loss: 0.1375 - acc: 0.9537 - val_loss: 0.0908 - val_acc: 1.0000\n",
      "Epoch 147/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1363 - acc: 0.9537 - val_loss: 0.0892 - val_acc: 1.0000\n",
      "Epoch 148/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1354 - acc: 0.9537 - val_loss: 0.0879 - val_acc: 1.0000\n",
      "Epoch 149/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1346 - acc: 0.9537 - val_loss: 0.0869 - val_acc: 1.0000\n",
      "Epoch 150/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1336 - acc: 0.9537 - val_loss: 0.0849 - val_acc: 1.0000\n",
      "Epoch 151/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.1326 - acc: 0.9537 - val_loss: 0.0836 - val_acc: 1.0000\n",
      "Epoch 152/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.1317 - acc: 0.9537 - val_loss: 0.0823 - val_acc: 1.0000\n",
      "Epoch 153/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.1313 - acc: 0.9537 - val_loss: 0.0821 - val_acc: 1.0000\n",
      "Epoch 154/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.1302 - acc: 0.9537 - val_loss: 0.0802 - val_acc: 1.0000\n",
      "Epoch 155/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1288 - acc: 0.9537 - val_loss: 0.0794 - val_acc: 1.0000\n",
      "Epoch 156/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1279 - acc: 0.9537 - val_loss: 0.0781 - val_acc: 1.0000\n",
      "Epoch 157/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.1271 - acc: 0.9537 - val_loss: 0.0773 - val_acc: 1.0000\n",
      "Epoch 158/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.1262 - acc: 0.9537 - val_loss: 0.0763 - val_acc: 1.0000\n",
      "Epoch 159/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1254 - acc: 0.9537 - val_loss: 0.0750 - val_acc: 1.0000\n",
      "Epoch 160/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1247 - acc: 0.9537 - val_loss: 0.0740 - val_acc: 1.0000\n",
      "Epoch 161/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1237 - acc: 0.9537 - val_loss: 0.0723 - val_acc: 1.0000\n",
      "Epoch 162/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1230 - acc: 0.9537 - val_loss: 0.0711 - val_acc: 1.0000\n",
      "Epoch 163/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1222 - acc: 0.9537 - val_loss: 0.0703 - val_acc: 1.0000\n",
      "Epoch 164/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.1213 - acc: 0.9537 - val_loss: 0.0688 - val_acc: 1.0000\n",
      "Epoch 165/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.1204 - acc: 0.9537 - val_loss: 0.0679 - val_acc: 1.0000\n",
      "Epoch 166/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.1197 - acc: 0.9537 - val_loss: 0.0669 - val_acc: 1.0000\n",
      "Epoch 167/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1195 - acc: 0.9537 - val_loss: 0.0656 - val_acc: 1.0000\n",
      "Epoch 168/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.1181 - acc: 0.9537 - val_loss: 0.0648 - val_acc: 1.0000\n",
      "Epoch 169/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.1176 - acc: 0.9630 - val_loss: 0.0643 - val_acc: 1.0000\n",
      "Epoch 170/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1169 - acc: 0.9537 - val_loss: 0.0633 - val_acc: 1.0000\n",
      "Epoch 171/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.1162 - acc: 0.9630 - val_loss: 0.0629 - val_acc: 1.0000\n",
      "Epoch 172/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.1153 - acc: 0.9630 - val_loss: 0.0619 - val_acc: 1.0000\n",
      "Epoch 173/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1146 - acc: 0.9722 - val_loss: 0.0609 - val_acc: 1.0000\n",
      "Epoch 174/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.1138 - acc: 0.9722 - val_loss: 0.0602 - val_acc: 1.0000\n",
      "Epoch 175/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1134 - acc: 0.9630 - val_loss: 0.0589 - val_acc: 1.0000\n",
      "Epoch 176/300\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.1126 - acc: 0.9630 - val_loss: 0.0581 - val_acc: 1.0000\n",
      "Epoch 177/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1120 - acc: 0.9630 - val_loss: 0.0570 - val_acc: 1.0000\n",
      "Epoch 178/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.1112 - acc: 0.9630 - val_loss: 0.0561 - val_acc: 1.0000\n",
      "Epoch 179/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1106 - acc: 0.9722 - val_loss: 0.0553 - val_acc: 1.0000\n",
      "Epoch 180/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.1097 - acc: 0.9722 - val_loss: 0.0549 - val_acc: 1.0000\n",
      "Epoch 181/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1091 - acc: 0.9722 - val_loss: 0.0545 - val_acc: 1.0000\n",
      "Epoch 182/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1085 - acc: 0.9722 - val_loss: 0.0539 - val_acc: 1.0000\n",
      "Epoch 183/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.1082 - acc: 0.9722 - val_loss: 0.0534 - val_acc: 1.0000\n",
      "Epoch 184/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.1073 - acc: 0.9722 - val_loss: 0.0525 - val_acc: 1.0000\n",
      "Epoch 185/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1069 - acc: 0.9722 - val_loss: 0.0512 - val_acc: 1.0000\n",
      "Epoch 186/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1063 - acc: 0.9722 - val_loss: 0.0506 - val_acc: 1.0000\n",
      "Epoch 187/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1056 - acc: 0.9722 - val_loss: 0.0497 - val_acc: 1.0000\n",
      "Epoch 188/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1049 - acc: 0.9722 - val_loss: 0.0490 - val_acc: 1.0000\n",
      "Epoch 189/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1045 - acc: 0.9722 - val_loss: 0.0480 - val_acc: 1.0000\n",
      "Epoch 190/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1040 - acc: 0.9722 - val_loss: 0.0477 - val_acc: 1.0000\n",
      "Epoch 191/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.1032 - acc: 0.9722 - val_loss: 0.0469 - val_acc: 1.0000\n",
      "Epoch 192/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1026 - acc: 0.9722 - val_loss: 0.0460 - val_acc: 1.0000\n",
      "Epoch 193/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.1022 - acc: 0.9722 - val_loss: 0.0454 - val_acc: 1.0000\n",
      "Epoch 194/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1015 - acc: 0.9722 - val_loss: 0.0445 - val_acc: 1.0000\n",
      "Epoch 195/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1009 - acc: 0.9722 - val_loss: 0.0439 - val_acc: 1.0000\n",
      "Epoch 196/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.1005 - acc: 0.9722 - val_loss: 0.0431 - val_acc: 1.0000\n",
      "Epoch 197/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.1004 - acc: 0.9722 - val_loss: 0.0429 - val_acc: 1.0000\n",
      "Epoch 198/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.0993 - acc: 0.9722 - val_loss: 0.0422 - val_acc: 1.0000\n",
      "Epoch 199/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.0987 - acc: 0.9722 - val_loss: 0.0416 - val_acc: 1.0000\n",
      "Epoch 200/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.0986 - acc: 0.9722 - val_loss: 0.0409 - val_acc: 1.0000\n",
      "Epoch 201/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.0978 - acc: 0.9722 - val_loss: 0.0404 - val_acc: 1.0000\n",
      "Epoch 202/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0972 - acc: 0.9722 - val_loss: 0.0400 - val_acc: 1.0000\n",
      "Epoch 203/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.0968 - acc: 0.9722 - val_loss: 0.0397 - val_acc: 1.0000\n",
      "Epoch 204/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.0967 - acc: 0.9722 - val_loss: 0.0396 - val_acc: 1.0000\n",
      "Epoch 205/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.0961 - acc: 0.9722 - val_loss: 0.0387 - val_acc: 1.0000\n",
      "Epoch 206/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.0953 - acc: 0.9722 - val_loss: 0.0382 - val_acc: 1.0000\n",
      "Epoch 207/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.0951 - acc: 0.9722 - val_loss: 0.0379 - val_acc: 1.0000\n",
      "Epoch 208/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.0945 - acc: 0.9722 - val_loss: 0.0371 - val_acc: 1.0000\n",
      "Epoch 209/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.0940 - acc: 0.9722 - val_loss: 0.0365 - val_acc: 1.0000\n",
      "Epoch 210/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.0934 - acc: 0.9722 - val_loss: 0.0360 - val_acc: 1.0000\n",
      "Epoch 211/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0931 - acc: 0.9722 - val_loss: 0.0358 - val_acc: 1.0000\n",
      "Epoch 212/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.0927 - acc: 0.9722 - val_loss: 0.0353 - val_acc: 1.0000\n",
      "Epoch 213/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.0922 - acc: 0.9722 - val_loss: 0.0347 - val_acc: 1.0000\n",
      "Epoch 214/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.0919 - acc: 0.9722 - val_loss: 0.0345 - val_acc: 1.0000\n",
      "Epoch 215/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0913 - acc: 0.9722 - val_loss: 0.0342 - val_acc: 1.0000\n",
      "Epoch 216/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.0913 - acc: 0.9722 - val_loss: 0.0333 - val_acc: 1.0000\n",
      "Epoch 217/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.0905 - acc: 0.9722 - val_loss: 0.0328 - val_acc: 1.0000\n",
      "Epoch 218/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.0901 - acc: 0.9722 - val_loss: 0.0325 - val_acc: 1.0000\n",
      "Epoch 219/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.0898 - acc: 0.9722 - val_loss: 0.0324 - val_acc: 1.0000\n",
      "Epoch 220/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.0894 - acc: 0.9722 - val_loss: 0.0318 - val_acc: 1.0000\n",
      "Epoch 221/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.0888 - acc: 0.9722 - val_loss: 0.0316 - val_acc: 1.0000\n",
      "Epoch 222/300\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0884 - acc: 0.9722 - val_loss: 0.0313 - val_acc: 1.0000\n",
      "Epoch 223/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0881 - acc: 0.9722 - val_loss: 0.0309 - val_acc: 1.0000\n",
      "Epoch 224/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.0882 - acc: 0.9722 - val_loss: 0.0302 - val_acc: 1.0000\n",
      "Epoch 225/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.0878 - acc: 0.9722 - val_loss: 0.0301 - val_acc: 1.0000\n",
      "Epoch 226/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.0869 - acc: 0.9722 - val_loss: 0.0296 - val_acc: 1.0000\n",
      "Epoch 227/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.0865 - acc: 0.9722 - val_loss: 0.0292 - val_acc: 1.0000\n",
      "Epoch 228/300\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0861 - acc: 0.9722 - val_loss: 0.0287 - val_acc: 1.0000\n",
      "Epoch 229/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.0859 - acc: 0.9722 - val_loss: 0.0283 - val_acc: 1.0000\n",
      "Epoch 230/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.0853 - acc: 0.9722 - val_loss: 0.0281 - val_acc: 1.0000\n",
      "Epoch 231/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0852 - acc: 0.9722 - val_loss: 0.0279 - val_acc: 1.0000\n",
      "Epoch 232/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.0851 - acc: 0.9722 - val_loss: 0.0273 - val_acc: 1.0000\n",
      "Epoch 233/300\n",
      "7/7 [==============================] - 0s 8ms/step - loss: 0.0845 - acc: 0.9722 - val_loss: 0.0273 - val_acc: 1.0000\n",
      "Epoch 234/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.0840 - acc: 0.9722 - val_loss: 0.0269 - val_acc: 1.0000\n",
      "Epoch 235/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.0836 - acc: 0.9722 - val_loss: 0.0266 - val_acc: 1.0000\n",
      "Epoch 236/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.0837 - acc: 0.9722 - val_loss: 0.0261 - val_acc: 1.0000\n",
      "Epoch 237/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.0829 - acc: 0.9722 - val_loss: 0.0258 - val_acc: 1.0000\n",
      "Epoch 238/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.0827 - acc: 0.9722 - val_loss: 0.0256 - val_acc: 1.0000\n",
      "Epoch 239/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0824 - acc: 0.9722 - val_loss: 0.0252 - val_acc: 1.0000\n",
      "Epoch 240/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0819 - acc: 0.9722 - val_loss: 0.0249 - val_acc: 1.0000\n",
      "Epoch 241/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0817 - acc: 0.9722 - val_loss: 0.0247 - val_acc: 1.0000\n",
      "Epoch 242/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0814 - acc: 0.9722 - val_loss: 0.0244 - val_acc: 1.0000\n",
      "Epoch 243/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0810 - acc: 0.9722 - val_loss: 0.0240 - val_acc: 1.0000\n",
      "Epoch 244/300\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0810 - acc: 0.9722 - val_loss: 0.0239 - val_acc: 1.0000\n",
      "Epoch 245/300\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0806 - acc: 0.9722 - val_loss: 0.0233 - val_acc: 1.0000\n",
      "Epoch 246/300\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0801 - acc: 0.9722 - val_loss: 0.0231 - val_acc: 1.0000\n",
      "Epoch 247/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0797 - acc: 0.9722 - val_loss: 0.0229 - val_acc: 1.0000\n",
      "Epoch 248/300\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0795 - acc: 0.9722 - val_loss: 0.0226 - val_acc: 1.0000\n",
      "Epoch 249/300\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0792 - acc: 0.9722 - val_loss: 0.0223 - val_acc: 1.0000\n",
      "Epoch 250/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0790 - acc: 0.9722 - val_loss: 0.0220 - val_acc: 1.0000\n",
      "Epoch 251/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0785 - acc: 0.9722 - val_loss: 0.0219 - val_acc: 1.0000\n",
      "Epoch 252/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0784 - acc: 0.9722 - val_loss: 0.0217 - val_acc: 1.0000\n",
      "Epoch 253/300\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0782 - acc: 0.9722 - val_loss: 0.0214 - val_acc: 1.0000\n",
      "Epoch 254/300\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0778 - acc: 0.9722 - val_loss: 0.0211 - val_acc: 1.0000\n",
      "Epoch 255/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0774 - acc: 0.9722 - val_loss: 0.0210 - val_acc: 1.0000\n",
      "Epoch 256/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0773 - acc: 0.9722 - val_loss: 0.0207 - val_acc: 1.0000\n",
      "Epoch 257/300\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0769 - acc: 0.9722 - val_loss: 0.0207 - val_acc: 1.0000\n",
      "Epoch 258/300\n",
      "7/7 [==============================] - 0s 14ms/step - loss: 0.0768 - acc: 0.9722 - val_loss: 0.0205 - val_acc: 1.0000\n",
      "Epoch 259/300\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0765 - acc: 0.9722 - val_loss: 0.0201 - val_acc: 1.0000\n",
      "Epoch 260/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0761 - acc: 0.9722 - val_loss: 0.0198 - val_acc: 1.0000\n",
      "Epoch 261/300\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0759 - acc: 0.9722 - val_loss: 0.0195 - val_acc: 1.0000\n",
      "Epoch 262/300\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0756 - acc: 0.9722 - val_loss: 0.0193 - val_acc: 1.0000\n",
      "Epoch 263/300\n",
      "7/7 [==============================] - 0s 19ms/step - loss: 0.0753 - acc: 0.9722 - val_loss: 0.0191 - val_acc: 1.0000\n",
      "Epoch 264/300\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0750 - acc: 0.9722 - val_loss: 0.0189 - val_acc: 1.0000\n",
      "Epoch 265/300\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0748 - acc: 0.9722 - val_loss: 0.0187 - val_acc: 1.0000\n",
      "Epoch 266/300\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0747 - acc: 0.9722 - val_loss: 0.0187 - val_acc: 1.0000\n",
      "Epoch 267/300\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0744 - acc: 0.9722 - val_loss: 0.0185 - val_acc: 1.0000\n",
      "Epoch 268/300\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0739 - acc: 0.9722 - val_loss: 0.0181 - val_acc: 1.0000\n",
      "Epoch 269/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0736 - acc: 0.9722 - val_loss: 0.0178 - val_acc: 1.0000\n",
      "Epoch 270/300\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0736 - acc: 0.9722 - val_loss: 0.0175 - val_acc: 1.0000\n",
      "Epoch 271/300\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0732 - acc: 0.9722 - val_loss: 0.0173 - val_acc: 1.0000\n",
      "Epoch 272/300\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0730 - acc: 0.9722 - val_loss: 0.0171 - val_acc: 1.0000\n",
      "Epoch 273/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0727 - acc: 0.9722 - val_loss: 0.0170 - val_acc: 1.0000\n",
      "Epoch 274/300\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0725 - acc: 0.9722 - val_loss: 0.0168 - val_acc: 1.0000\n",
      "Epoch 275/300\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0723 - acc: 0.9722 - val_loss: 0.0167 - val_acc: 1.0000\n",
      "Epoch 276/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0721 - acc: 0.9722 - val_loss: 0.0163 - val_acc: 1.0000\n",
      "Epoch 277/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0717 - acc: 0.9722 - val_loss: 0.0162 - val_acc: 1.0000\n",
      "Epoch 278/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0714 - acc: 0.9722 - val_loss: 0.0160 - val_acc: 1.0000\n",
      "Epoch 279/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.0717 - acc: 0.9722 - val_loss: 0.0160 - val_acc: 1.0000\n",
      "Epoch 280/300\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0710 - acc: 0.9722 - val_loss: 0.0157 - val_acc: 1.0000\n",
      "Epoch 281/300\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0707 - acc: 0.9722 - val_loss: 0.0155 - val_acc: 1.0000\n",
      "Epoch 282/300\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0707 - acc: 0.9722 - val_loss: 0.0153 - val_acc: 1.0000\n",
      "Epoch 283/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0703 - acc: 0.9722 - val_loss: 0.0152 - val_acc: 1.0000\n",
      "Epoch 284/300\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0703 - acc: 0.9722 - val_loss: 0.0149 - val_acc: 1.0000\n",
      "Epoch 285/300\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0700 - acc: 0.9722 - val_loss: 0.0149 - val_acc: 1.0000\n",
      "Epoch 286/300\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0697 - acc: 0.9722 - val_loss: 0.0148 - val_acc: 1.0000\n",
      "Epoch 287/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.0696 - acc: 0.9722 - val_loss: 0.0147 - val_acc: 1.0000\n",
      "Epoch 288/300\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0692 - acc: 0.9722 - val_loss: 0.0146 - val_acc: 1.0000\n",
      "Epoch 289/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0693 - acc: 0.9722 - val_loss: 0.0145 - val_acc: 1.0000\n",
      "Epoch 290/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0689 - acc: 0.9722 - val_loss: 0.0142 - val_acc: 1.0000\n",
      "Epoch 291/300\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0686 - acc: 0.9722 - val_loss: 0.0140 - val_acc: 1.0000\n",
      "Epoch 292/300\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0686 - acc: 0.9722 - val_loss: 0.0139 - val_acc: 1.0000\n",
      "Epoch 293/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0681 - acc: 0.9722 - val_loss: 0.0138 - val_acc: 1.0000\n",
      "Epoch 294/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0680 - acc: 0.9722 - val_loss: 0.0137 - val_acc: 1.0000\n",
      "Epoch 295/300\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0679 - acc: 0.9722 - val_loss: 0.0136 - val_acc: 1.0000\n",
      "Epoch 296/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0675 - acc: 0.9722 - val_loss: 0.0134 - val_acc: 1.0000\n",
      "Epoch 297/300\n",
      "7/7 [==============================] - 0s 11ms/step - loss: 0.0675 - acc: 0.9722 - val_loss: 0.0132 - val_acc: 1.0000\n",
      "Epoch 298/300\n",
      "7/7 [==============================] - 0s 9ms/step - loss: 0.0672 - acc: 0.9722 - val_loss: 0.0131 - val_acc: 1.0000\n",
      "Epoch 299/300\n",
      "7/7 [==============================] - 0s 12ms/step - loss: 0.0670 - acc: 0.9722 - val_loss: 0.0129 - val_acc: 1.0000\n",
      "Epoch 300/300\n",
      "7/7 [==============================] - 0s 10ms/step - loss: 0.0668 - acc: 0.9722 - val_loss: 0.0128 - val_acc: 1.0000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import keras\n",
    "from keras import layers\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from keras.utils import np_utils\n",
    "\n",
    "data = pd.read_csv(\"dataset/iris_training.csv\", header=0)\n",
    "data.columns = ['l1', 'l2', 'l3', 'l4', 'lei']\n",
    "\n",
    "data = np.array(data)\n",
    "x_train = data[:110, :-1]\n",
    "y_train = data[:110, -1]\n",
    "x_test = data[110:, :-1]\n",
    "y_test = data[110:, -1]\n",
    "\n",
    "mean = x_train.mean(axis=0)\n",
    "std = x_train.std(axis=0)\n",
    "x_train = (x_train - mean) / std\n",
    "\n",
    "mean = x_test.mean(axis=0)\n",
    "std = x_test.std(axis=0)\n",
    "x_test = (x_test - mean) / std\n",
    "\n",
    "y_train = np_utils.to_categorical(y_train, num_classes=3)\n",
    "y_test = np_utils.to_categorical(y_test, num_classes=3)\n",
    "\n",
    "model = keras.Sequential()\n",
    "model.add(layers.Dense(16, input_dim=4, activation='relu'))\n",
    "model.add(layers.Dense(3, activation='softmax'))\n",
    "\n",
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc'])\n",
    "\n",
    "history = model.fit(x_train, y_train, epochs=300, verbose=1, batch_size=16, validation_split=0.01)\n",
    "\n",
    "plt.plot(history.history.get('loss'))\n",
    "plt.plot(history.history.get('acc'))\n",
    "plt.legend([\"loss\", \"acc\"])\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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